Create README.md

README.md

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+# tensorflow-vgg
+Tensorflow implementation of VGG 16 and VGG 19
+
+This is a implemention of VGG 16 and VGG 19 based on <a href="https://github.com/ry/tensorflow-vgg16">tensorflow-vgg16</a> and <a href="https://github.com/ethereon/caffe-tensorflow">Caffe to Tensorflow</a>.
+
+We have modified the implementation of <a href="https://github.com/ry/tensorflow-vgg16">tensorflow-vgg16</a> to use numpy loading instead of default tensorflow model loading in order to speed up the initialisation and reduct overall memory usage. This implementation enable further modify the network, e.g. remove the FC layers, or increase the batch size.
+
+To use the VGG networks, the npy files for <a href="https://dl.dropboxusercontent.com/u/50333326/vgg16.npy">VGG16</a> or <a href="https://dl.dropboxusercontent.com/u/50333326/vgg19.npy">VGG19</a> has to be downloaded.
+
+#Usage
+Use this to build the VGG object
+
+	vgg = vgg19.Vgg19()
+	vgg.build(images)
+
+or
+
+	vgg = vgg16.Vgg16()
+	vgg.build(images)
+
+The images is a tensor with shape `[2, 224, 224, 3]`. (Trick: the tensor can be a placeholder, a variable or even a constant).
+
+All the VGG layers, as tensors, can then be accessed using the vgg object. For example, `vgg.conv1_1`, `vgg.conv1_2`, `vgg.pool5`, `vgg.prob`, ...
+
+`test_vgg16.py` and `test_vgg19.py` contain the sample usage.